Fast, general-purpose classifiers like Jev can be used as temporary solutions for specific tasks, then replaced with cheaper, faster custom classifiers trained on the input-output data the Jev instance generated. This creates a practical path for engineering teams without ML expertise to eventually build optimized task-specific models.

• System One models solve the immediate problem of deploying classifiers without ML expertise or large datasets — you just prompt them like an LLM
• Once a System One model proves a feature works, you can collect its decisions as training data to build a specialized classifier that's faster and cheaper to run
• This pattern lets teams validate whether a feature is worth building before investing in custom ML development, reducing risk and upfront cost